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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target\n",
    "\n",
    "![](http://r-statistics.co/screenshots/ggplot_masterlist_9.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "mpg= pd.read_csv(\"http://goo.gl/uEeRGu\") \n",
    "mpg_corr  = mpg.corr().round(2)"
   ]
  },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>displ</th>\n",
       "      <th>year</th>\n",
       "      <th>cyl</th>\n",
       "      <th>cty</th>\n",
       "      <th>hwy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>displ</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.93</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.15</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl</th>\n",
       "      <td>0.93</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cty</th>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hwy</th>\n",
       "      <td>-0.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       displ  year   cyl   cty   hwy\n",
       "displ   1.00  0.15  0.93 -0.80 -0.77\n",
       "year    0.15  1.00  0.12 -0.04  0.00\n",
       "cyl     0.93  0.12  1.00 -0.81 -0.76\n",
       "cty    -0.80 -0.04 -0.81  1.00  0.96\n",
       "hwy    -0.77  0.00 -0.76  0.96  1.00"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>displ</td>\n",
       "      <td>displ</td>\n",
       "      <td>1.00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>displ</td>\n",
       "      <td>year</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>displ</td>\n",
       "      <td>cyl</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>displ</td>\n",
       "      <td>cty</td>\n",
       "      <td>-0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>displ</td>\n",
       "      <td>hwy</td>\n",
       "      <td>-0.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       x      y  value\n",
       "0  displ  displ   1.00\n",
       "1  displ   year   0.15\n",
       "2  displ    cyl   0.93\n",
       "3  displ    cty  -0.80\n",
       "4  displ    hwy  -0.77"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_corr = mpg_corr.stack().reset_index()\n",
    "mpg_corr.columns = ['x','y','value']\n",
    "mpg_corr.head()"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
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"
     },
     "metadata": {
      "jupyter-vega": "#0b67b15b-9334-4408-8b25-4efaefed41e0"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = Chart(mpg_corr).mark_text(color='black', fontSize=12, fontWeight='bold').encode(x=X('x', \n",
    "                                                                                            axis=Axis(labelAngle=-45)),\n",
    "                                                                                            y='y', text='value',) + \\\n",
    "        Chart(mpg_corr).mark_point(color='black:Q', size=500, filled=True).encode(x='x', y='y', color=Color('value',\n",
    "                                                                                                           scale= Scale(range=[\"red\", \"green\"])))\n",
    "\n",
    "chart.data = mpg_corr\n",
    "chart.configure(numberFormat=\"0.2f\")\n",
    "chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Todo\n",
    "\n",
    "1. Can we make the plot look nicer? This looks illegible at the moment!\n",
    "2. How about a better colormap for showing the gradient \n",
    "3. How about making the legend vertical instead of horizontal?\n",
    "4. How about size defaults for font and circle for such plots, specified as a percentage? Had to fiddle around a lot to make these plots.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
